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Temporal accumulation analysis provides simplified artifact-free analysis of membrane-protein nanoclusters

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Figure 1: Temporal accumulation analysis of a single SMLM data set provides robust information on protein clustering.


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The authors wish to thank F. Baumgart and G. Schütz for providing data and F. Fricke and C. Böger for helpful discussions. This work was supported by the German Science Foundation through the CellNetworks Cluster of Excellence (EXC 81 to T.K.) and the Cluster of Excellence Macromolecular Complexes (EXC 115 to M.H.).

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Correspondence to Mike Heilemann.

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Integrated supplementary information

Supplementary Figure 1 Influences of various strategies for subset generation using temporal accumulation on ρ/ρ0 versus η plots.

(A) Experimental PALM data sets were either split randomly without overlap with previous frames (black), randomly with overlap (red) or consecutively (blue), and used to generate ρ/ρ0 versus η plots. (B) ρ/ρ0 versus η plots for different partitioning methods for the same data sets as in figure (A) but with background localizations removed by filtering (see methods). (C) Histogram of single-molecule localizations (counts) over frames with red lines indicating the upper borders of the data sub-sets generated for temporal accumulation analysis.

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Supplementary Figure 2 Determination of ρ0 from simulated SMLM data of clustered and randomly distributed mEos2 and Alexa Fluor 647 molecules.

(A-D) Simulated SMLM data of 6 clusters per μm2 with 50 nm radius and containing 70 epitopes, using the mEos2 blinking model (A, B) or the Alexa Fluor 647 blinking model (C, D), before (A, C) and after normalization (B, D) for the different thresholds shown. (E-H) Simulated SMLM data for randomly distributed mEos2 (E, F) and Alexa Fluor 647 (G, H), before (E, G) and after normalization (F, H) for the different thresholds shown.

Source data

Supplementary Figure 3 Analysis of data published by Baumgart et al. using a custom-written Fiji macro.

(A) Analysis of the micro-contact printing data (Figure 2a in Baumgart et al.) using the different thresholds indicated. (B) Comparison of the temporal accumulation analysis with the original analysis (Baumgart et al.) for a threshold of 0.03. (C-F) Fiji analysis (black) compared to the analysis by Baumgart et al. (red) for (C) random distribution, (D) clathrin-HC, (E) GPI-mGFP labeled with GFP-Trap-AF647 and (F) GPI-mEos3.2. Blue arrows indicate the data points from Baumgart et al. that were each split into four 5 × 5 μm2 regions and analyzed using our custom-written Fiji macro.

Source data

Supplementary Figure 4 Cluster analysis of mEos2–clathrin light chain (CLC) using temporal accumulation analysis.

(A) PALM images of cells showing endocytosis-mediated clathrin-coated pits formation or (B) a random distribution of mEos2-CLC on the basal membrane. (C) Temporal accumulation analysis of the two regions (i, ii) in (A) and the resulting ρ/ρ0 versus η plot indicate clustering. (D) Temporal accumulation analysis of regions (i, ii) from (B) and the resulting ρ/ρ0 versus η plot indicate a random distribution. (E) To investigate the influence of background signal in (B), the SMLM data was filtered with DBSCAN (see methods) 5, and the ρ/ρ0 versus η plot generated (scale bars are 3 μm (A), 5 μm (B), and 1 μm (insets in (A), (B)).

Source data

Supplementary Figure 5 Labeling of clathrin heavy chain at different densities by titration of Alexa Fluor 647 conjugated anti-CHC antibody.

(A) Titration curve obtained from brightness analysis of confocal microscopy images (shown are the mean ± s.d., n = 12 for all concentrations, see methods). (B) Representative dSTORM images of clathrin-coated pits labeled with different antibody concentrations. (C) ρ/ρ0 plotted versus η for the titration experiment using the analysis from Baumgart et al. (D) Temporal accumulation analysis of the same data sets as in (C). (E) SMLM data in (D) filtered with DBSCAN and (F) analyzed by temporal accumulation (scale bars 0.5 μm (B), 1 μm (E)).

Source data

Supplementary Figure 6 Influence of background localizations on plots of ρ/ρ0 versus η curves.

6 clusters (50 nm radius, 70 epitopes) per μm2 were simulated with increasing background levels. In the absence of background (dark blue circles), strong clustering is observed, while increasing background yields curves shifted towards random distribution (red solid line) (for a detailed discussion see Supplementary Notes).

Source data

Supplementary Figure 7 Determination of epitope labeling efficiency of clathrin-coated pits using Alexa Fluor 647-conjugated anti-CHC antibodies.

(A) SMLM images of clathrin-HC labeled with varying antibody concentrations. Images are shown Gaussian-blurred (left row), as single localizations (middle row) and filtered for cluster size (right; red clusters indicate single antibodies identified via the Fiji plugin "Analyze Particles", see methods). (B) The number of single-molecule localizations per single antibody label (red clusters in (A)) was determined and found constant for all antibody concentrations (shown are the mean ± s.d., n ranging between 227 and 411) . (C) Single clathrincoated pits were analyzed for the number of bound antibodies per clathrin-coated pit (n = 490, see methods for analysis parameters). (D) Magnified view of three ROIs (yellow squares in (C)) indicating the number of antibodies bound (red numbers) calculated from the number of localizations in single identified clathrin-coated pits. On average, 18.4 ± 9.8 antibodies were bound to single clathrin-coated pits, yielding an estimated labeling efficiency of around 20-25% (see Supplementary Notes for a more detailed description). (E) Temporal accumulation analysis for the 5 ROIs from Fig. S5, (i) unfiltered and (ii) background filtered using DBSCAN; (iii) realistic simulation of clathrin-coated pits labeled with anti clathrin-HC-Alexa Fluor 647 using the determined parameters for size, number of epitopes, labeling efficiencies and blinking statistics (6 clusters per μm2 were simulated, background level of 20% ) (scale bar 250 nm (A)).

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Supplementary Figures 1–7 and Supplementary Note 1 (PDF 4250 kb)

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Spahn, C., Herrmannsdörfer, F., Kuner, T. et al. Temporal accumulation analysis provides simplified artifact-free analysis of membrane-protein nanoclusters. Nat Methods 13, 963–964 (2016).

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